A novel visualization system for ICU clinical activity tracking

Peng Guo, Yeong Shiong Chiew, Lei Shao, Adrian Clark, J. Geoffrey Chase

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther


Patient and nurse interaction in the Intensive Care Unit (ICU) is important as it influences the patient outcomes. Optimizing the nurse-to-patient ratio can reduce the mortality of patient, prevent nurse burnout, and reduce costs in the ICU. However, there is a lack of methods to quantify and evaluate these bedside interactions. This paper presents a Clinical Activity Tracking System (CATS), which was designed to track and evaluate nursing motion at the patient bedside, aiming to quantify how nurses spent their working time. CATS utilizes the Microsoft Kinect, a motion sensing device containing an embedded camera and infrared sensor. For CATS, the Kinect is fixed on the ceiling, facing downwards to track clinical activity at the patient bedside. The system was set up in an experimental environment to simulate the ICU bedside activity, and different motion paths and test candidates were tested over 5 iterations to evaluate the performance of the system. The total tracking area for the CATS can reach 2.3 m × 1.6 m, which mimics to the ICU bedside area. The system can track candidates with different heights from 1.52 m to 1.90 m. The system can also track different motion patterns consistently, with median percentage tracking error 2.30% (Inter-quartile range (IQR): [0.72%, 4.25%]). The system can also track multiple candidates with median percentage error 1.75% (Inter-quartile range (IQR): 0.97%, 4.57%). The results show that the system can be used in real-time applications to track bedside clinical activity. This system is capable of evaluating the ICU nursing activity, with the ultimate aim to generate appropriate nurse-to-patient ratio to prevent nurse burnout and increase patient care. Also, it is able to track different candidate heights, adapt to different motion paths, different dwell time, and identify multiple people simultaneously. The results revealed that the system can be used to quantify and evaluate bedside clinical activity.

Original languageEnglish
Title of host publication19th IFAC World Congress IFAC 2014, Proceedings
EditorsEdward Boje, Xiaohua Xia
PublisherElsevier - International Federation of Automatic Control (IFAC)
Number of pages6
ISBN (Electronic)9783902823625
Publication statusPublished - 2014
Externally publishedYes
EventInternational Federation of Automatic Control World Congress 2014 - Cape Town International Convention Centre, Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014
Conference number: 19th

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
ISSN (Print)1474-6670


ConferenceInternational Federation of Automatic Control World Congress 2014
Abbreviated titleIFAC 2014
Country/TerritorySouth Africa
CityCape Town
Internet address

Cite this